Neural information retrieval (IR) has become a promising research direction for improving traditional IR systems.
The most-commonly adopted approach called the dual encoder operates by representing every query and document as a single dense vector.
Given sufficient annotations, dual encoders directly learn task-driven similarity between vectors, and often surpass traditional IR systems on complex tasks such as question answering [1]}, [2]}, [3]}.
However, these models can struggle to generalize over out-of-domain datasets [4]} and/or entity-centric questions [5]} due to the limited representational capacity of single vectors.
As a remedy, multi-vector retrieval models [6]}, [7]}, [8]} instead use multiple vectors, typically the contextualized token vectors, to represent the text.
These models largely improve the model expressiveness, and exhibit much stronger performance and robustness compared to their single-vector counterparts.
